"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=3      ,					_lowerCamelCase=224      ,					_lowerCamelCase=30      ,					_lowerCamelCase=400      ,					_lowerCamelCase=True      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=[0.5, 0.5, 0.5]      ,					_lowerCamelCase=[0.5, 0.5, 0.5]      ,					):
										lowerCamelCase__   =size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =num_channels
										lowerCamelCase__   =image_size
										lowerCamelCase__   =min_resolution
										lowerCamelCase__   =max_resolution
										lowerCamelCase__   =do_resize
										lowerCamelCase__   =size
										lowerCamelCase__   =do_normalize
										lowerCamelCase__   =image_mean
										lowerCamelCase__   =image_std






				def 						_a      ( self ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}






@require_torch
@require_vision
class   __UpperCAmelCase		(      _UpperCAmelCase , unittest.TestCase      ):
				A__ :  Tuple    				=			ViTImageProcessor if is_vision_available() else None

				def 						_a      ( self ):
										lowerCamelCase__   =EfficientFormerImageProcessorTester(self )

				@property
				def 						_a      ( self ):
										return self.image_proc_tester.prepare_image_processor_dict()

				def 						_a      ( self ):
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(lowerCAmelCase__      ,					"image_mean" ) )
										self.assertTrue(hasattr(lowerCAmelCase__      ,					"image_std" ) )
										self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_normalize" ) )
										self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_resize" ) )
										self.assertTrue(hasattr(lowerCAmelCase__      ,					"size" ) )

				def 						_a      ( self ):
										pass

				def 						_a      ( self ):
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=lowerCAmelCase__ )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,					Image.Image )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(lowerCAmelCase__      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

				def 						_a      ( self ):
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=lowerCAmelCase__      ,					numpify=lowerCAmelCase__ )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,					np.ndarray )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(lowerCAmelCase__      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)






				def 						_a      ( self ):
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=lowerCAmelCase__      ,					torchify=lowerCAmelCase__ )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,					torch.Tensor )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(lowerCAmelCase__      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings